Variable labels

Xem 1-14 trên 14 kết quả Variable labels
  • We consider a semi-supervised setting for domain adaptation where only unlabeled data is available for the target domain. One way to tackle this problem is to train a generative model with latent variables on the mixture of data from the source and target domains. Such a model would cluster features in both domains and ensure that at least some of the latent variables are predictive of the label on the source domain.

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  • The present paper describes a robust approach for abbreviating terms. First, in order to incorporate non-local information into abbreviation generation tasks, we present both implicit and explicit solutions: the latent variable model, or alternatively, the label encoding approach with global information. Although the two approaches compete with one another, we demonstrate that these approaches are also complementary. By combining these two approaches, experiments revealed that the proposed abbreviation generator achieved the best results for both the Chinese and English languages. ...

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  • We derive two variants of a semi-supervised model for fine-grained sentiment analysis. Both models leverage abundant natural supervision in the form of review ratings, as well as a small amount of manually crafted sentence labels, to learn sentence-level sentiment classifiers. The proposed model is a fusion of a fully supervised structured conditional model and its partially supervised counterpart. This allows for highly efficient estimation and inference algorithms with rich feature definitions. ...

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  • Latent conditional models have become popular recently in both natural language processing and vision processing communities. However, establishing an effective and efficient inference method on latent conditional models remains a question. In this paper, we describe the latent-dynamic inference (LDI), which is able to produce the optimal label sequence on latent conditional models by using efficient search strategy and dynamic programming.

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  • SPSS Statistics provides a powerful statistical-analysis and data-management system in a graphical environment, using descriptive menus and simple dialog boxes to do most of the work for you. Most tasks can be accomplished simply by pointing and clicking the mouse. In addition to the simple point-and-click interface for statistical analysis, SPSS Statistics provides: Data Editor. The Data Editor is a versatile spreadsheet-like system for defining, entering, editing, and displaying data.

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  • The present book is a collection of ten original research articles and reports, associated with selected topics in agricultural chemistry. The discussed issues are organized in four sections: Classification and labeling of active substances in plant protection products, Environmental and stress plant physiology and behavior, Antimicrobial and antioxidant potential of plant extracts, and Pollutants analysis and effects. The information provided in this book should be of interest for academic researchers and for agriculturalists....

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  • Chapter 19 EXAMPLES AND ADDENDA The separate sections of this chapter are not related to one another except in so far as they illustrate or extend the results of Chapter 18 . © 1 . The central limit theorem for homogeneous Markov chains Consider a homogeneous Markov chain with a finite number of states (labelled 1, 2, . . ., k) and transition matrix P = (p i ;) (see, for instance, Chapter III of [47] ) . If Xn is the state of the system at time n, we have the sequence of random variables X1 , X2 , . . ., Xn...

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  • In the area of sport and exercise students and researchers often face important questions. For example, in sport psychology, a student may be interested in examining whether the pre-competitive anxiety levels of a group of athletes can be predicted by a number of psychological variables. In exercise physiology, another student may want to examine the degree to which a particular training programme has improved the aerobic capacity of a group of runners.

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  • A Simple Site or Application Structure 1 2 3 4 5 6 7 8 9 10 11 12 var nextSection:String = ""; section1.addEventListener(MouseEvent.CLICK, navigate, false, 0, true); section2.addEventListener(MouseEvent.CLICK, navigate, false, 0, true); section3.addEventListener(MouseEvent.CLICK, navigate, false, 0, true); function navigate(evt:MouseEvent):void { nextSection = evt.target.

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  • In a classification problem, you typically have historical data (labeled examples) and unlabeled examples. Each labeled example consists of multiple predictor attributes and one target attribute (dependent variable). The value of the target attribute is a class label. The unlabeled examples consist of the predictor attributes only. The goal of classification is to construct a model using the historical data that accurately predicts the label (class) of the unlabeled examples.

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  • This paper presents a novel sequence labeling model based on the latent-variable semiMarkov conditional random fields for jointly extracting argument roles of events from texts. The model takes in coarse mention and type information and predicts argument roles for a given event template. This paper addresses the event extraction problem in a primarily unsupervised setting, where no labeled training instances are available.

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  • We describe two probabilistic models for unsupervised word-sense disambiguation using parallel corpora. The first model, which we call the Sense model, builds on the work of Diab and Resnik (2002) that uses both parallel text and a sense inventory for the target language, and recasts their approach in a probabilistic framework. The second model, which we call the Concept model, is a hierarchical model that uses a concept latent variable to relate different language specific sense labels.

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  • Unification of disjunctive feature descriptions is important for efficient unification-based parsing. This paper presents constraint projection, a new method for unification of disjunctive feature structures represented by logical constraints. Constraint projection is a generalization of constraint unification, and is more efficient because constraint projection has a mechanism for abandoning information irrelevant to a goal specified by a list of variables. These works are based on graph unification rather than on term unification.

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  • An extension to the GPSG grammatical formalism is solution. proposed, finite allowing non-terminals to consist of sentences sequences of category labels, and allowing those schematic variables to range over such sequences. appropriate.

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